<p>
  In this research, We investigate two pairs trading methods and compare the result. Pairs trading involves in investigating the dependence structure between two highly correlated assets. With the assumption that mean reversion will occur, long or short positions are entered in the opposite direction when there is a price divergence. Typically the asset price distribution is modeled by a  Gaussian distribution of return series but the joint normal distribution may fail to catch some key features of the dependence of stock pairs' price like tail dependence. We investigate using copula theory to identify these trading opportunities.
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<p>
  We will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a></em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012). The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
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